Overview

Dataset statistics

Number of variables19
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 KiB
Average record size in memory213.5 B

Variable types

Text1
Numeric18

Alerts

CochesVendidos_2017 is highly overall correlated with CochesVendidos_2018 and 4 other fieldsHigh correlation
CochesVendidos_2018 is highly overall correlated with CochesVendidos_2017 and 4 other fieldsHigh correlation
CochesVendidos_2019 is highly overall correlated with CochesVendidos_2017 and 4 other fieldsHigh correlation
CochesVendidos_2020 is highly overall correlated with CochesVendidos_2017 and 4 other fieldsHigh correlation
CochesVendidos_2021 is highly overall correlated with CochesVendidos_2017 and 4 other fieldsHigh correlation
CochesVendidos_2022 is highly overall correlated with CochesVendidos_2017 and 4 other fieldsHigh correlation
Diesel_2017 is highly overall correlated with Diesel_2018 and 10 other fieldsHigh correlation
Diesel_2018 is highly overall correlated with Diesel_2017 and 10 other fieldsHigh correlation
Diesel_2019 is highly overall correlated with Diesel_2017 and 10 other fieldsHigh correlation
Diesel_2020 is highly overall correlated with Diesel_2017 and 10 other fieldsHigh correlation
Diesel_2021 is highly overall correlated with Diesel_2017 and 10 other fieldsHigh correlation
Diesel_2022 is highly overall correlated with Diesel_2017 and 10 other fieldsHigh correlation
Gasolina_2017 is highly overall correlated with Diesel_2017 and 10 other fieldsHigh correlation
Gasolina_2018 is highly overall correlated with Diesel_2017 and 10 other fieldsHigh correlation
Gasolina_2019 is highly overall correlated with Diesel_2017 and 10 other fieldsHigh correlation
Gasolina_2020 is highly overall correlated with Diesel_2017 and 10 other fieldsHigh correlation
Gasolina_2021 is highly overall correlated with Diesel_2017 and 10 other fieldsHigh correlation
Gasolina_2022 is highly overall correlated with Diesel_2017 and 10 other fieldsHigh correlation
Country has unique valuesUnique
CochesVendidos_2017 has unique valuesUnique
CochesVendidos_2018 has unique valuesUnique
CochesVendidos_2019 has unique valuesUnique
CochesVendidos_2020 has unique valuesUnique
CochesVendidos_2021 has unique valuesUnique
CochesVendidos_2022 has unique valuesUnique
Diesel_2017 has unique valuesUnique
Gasolina_2019 has unique valuesUnique
Gasolina_2020 has unique valuesUnique
Diesel_2020 has unique valuesUnique
Gasolina_2021 has unique valuesUnique
Diesel_2021 has unique valuesUnique
Gasolina_2022 has unique valuesUnique
Diesel_2022 has unique valuesUnique

Reproduction

Analysis started2023-11-27 17:18:09.095934
Analysis finished2023-11-27 17:18:49.691510
Duration40.6 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Country
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-11-27T18:18:49.855582image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length14
Median length11
Mean length7.0416667
Min length2

Characters and Unicode

Total characters169
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st rowAustria
2nd rowBulgaria
3rd rowCroatia
4th rowCzech Republic
5th rowDenmark
ValueCountFrequency (%)
austria 1
 
4.0%
ireland 1
 
4.0%
croatia 1
 
4.0%
republic 1
 
4.0%
czech 1
 
4.0%
denmark 1
 
4.0%
estonia 1
 
4.0%
finland 1
 
4.0%
france 1
 
4.0%
germany 1
 
4.0%
Other values (15) 15
60.0%
2023-11-27T18:18:50.214495image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 22
 
13.0%
e 15
 
8.9%
n 14
 
8.3%
r 12
 
7.1%
i 10
 
5.9%
l 10
 
5.9%
o 8
 
4.7%
u 7
 
4.1%
t 6
 
3.6%
S 5
 
3.0%
Other values (30) 60
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 140
82.8%
Uppercase Letter 28
 
16.6%
Space Separator 1
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 22
15.7%
e 15
10.7%
n 14
10.0%
r 12
 
8.6%
i 10
 
7.1%
l 10
 
7.1%
o 8
 
5.7%
u 7
 
5.0%
t 6
 
4.3%
d 5
 
3.6%
Other values (13) 31
22.1%
Uppercase Letter
ValueCountFrequency (%)
S 5
17.9%
G 2
 
7.1%
F 2
 
7.1%
C 2
 
7.1%
I 2
 
7.1%
A 2
 
7.1%
P 2
 
7.1%
R 2
 
7.1%
U 2
 
7.1%
N 1
 
3.6%
Other values (6) 6
21.4%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 168
99.4%
Common 1
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 22
 
13.1%
e 15
 
8.9%
n 14
 
8.3%
r 12
 
7.1%
i 10
 
6.0%
l 10
 
6.0%
o 8
 
4.8%
u 7
 
4.2%
t 6
 
3.6%
S 5
 
3.0%
Other values (29) 59
35.1%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 169
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 22
 
13.0%
e 15
 
8.9%
n 14
 
8.3%
r 12
 
7.1%
i 10
 
5.9%
l 10
 
5.9%
o 8
 
4.7%
u 7
 
4.1%
t 6
 
3.6%
S 5
 
3.0%
Other values (30) 60
35.5%

CochesVendidos_2017
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1307439.6
Minimum24223
Maximum16188680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size324.0 B
2023-11-27T18:18:50.386514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum24223
5-th percentile27748.5
Q198353.5
median247561.5
Q3697820.5
95-th percentile3301350.1
Maximum16188680
Range16164457
Interquartile range (IQR)599467

Descriptive statistics

Standard deviation3313933.8
Coefficient of variation (CV)2.5346744
Kurtosis19.550729
Mean1307439.6
Median Absolute Deviation (MAD)167186
Skewness4.277722
Sum31378551
Variance1.0982157 × 1013
MonotonicityNot monotonic
2023-11-27T18:18:50.553931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
379184 1
 
4.2%
24223 1
 
4.2%
2630610 1
 
4.2%
346951 1
 
4.2%
1372519 1
 
4.2%
70304 1
 
4.2%
100989 1
 
4.2%
146278 1
 
4.2%
243360 1
 
4.2%
472921 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
24223 1
4.2%
24834 1
4.2%
44264 1
4.2%
50495 1
4.2%
70304 1
4.2%
90447 1
4.2%
100989 1
4.2%
101057 1
4.2%
122464 1
4.2%
146278 1
4.2%
ValueCountFrequency (%)
16188680 1
4.2%
3419716 1
4.2%
2630610 1
4.2%
2439778 1
4.2%
2037877 1
4.2%
1372519 1
4.2%
472921 1
4.2%
386442 1
4.2%
379184 1
4.2%
346951 1
4.2%

CochesVendidos_2018
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1314205.3
Minimum25581
Maximum16263975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size324.0 B
2023-11-27T18:18:50.713356image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum25581
5-th percentile33549.2
Q1105340.75
median247962
Q3755837
95-th percentile3291973.9
Maximum16263975
Range16238394
Interquartile range (IQR)650496.25

Descriptive statistics

Standard deviation3324973.9
Coefficient of variation (CV)2.5300262
Kurtosis19.666754
Mean1314205.3
Median Absolute Deviation (MAD)168995
Skewness4.2935168
Sum31540927
Variance1.1055451 × 1013
MonotonicityNot monotonic
2023-11-27T18:18:50.894420image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
370315 1
 
4.2%
30218 1
 
4.2%
2458022 1
 
4.2%
329629 1
 
4.2%
1478681 1
 
4.2%
72013 1
 
4.2%
104407 1
 
4.2%
175992 1
 
4.2%
251235 1
 
4.2%
514889 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
25581 1
4.2%
30218 1
4.2%
52426 1
4.2%
58557 1
4.2%
72013 1
4.2%
104407 1
4.2%
105652 1
4.2%
125618 1
4.2%
131594 1
4.2%
146742 1
4.2%
ValueCountFrequency (%)
16263975 1
4.2%
3428367 1
4.2%
2519080 1
4.2%
2458022 1
4.2%
1971108 1
4.2%
1478681 1
4.2%
514889 1
4.2%
410003 1
4.2%
370315 1
4.2%
329629 1
4.2%

CochesVendidos_2019
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1307860.1
Minimum26839
Maximum15956729
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size324.0 B
2023-11-27T18:18:51.085465image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum26839
5-th percentile35015.5
Q1114207
median247380
Q3757287.75
95-th percentile3440316.1
Maximum15956729
Range15929890
Interquartile range (IQR)643080.75

Descriptive statistics

Standard deviation3269060.7
Coefficient of variation (CV)2.4995493
Kurtosis19.362491
Mean1307860.1
Median Absolute Deviation (MAD)171943
Skewness4.2535446
Sum31388642
Variance1.0686758 × 1013
MonotonicityNot monotonic
2023-11-27T18:18:51.349758image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
358175 1
 
4.2%
31726 1
 
4.2%
2410472 1
 
4.2%
337615 1
 
4.2%
1415709 1
 
4.2%
71850 1
 
4.2%
106875 1
 
4.2%
190399 1
 
4.2%
245511 1
 
4.2%
537814 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
26839 1
4.2%
31726 1
4.2%
53656 1
4.2%
61707 1
4.2%
71850 1
4.2%
106875 1
4.2%
116651 1
4.2%
117691 1
4.2%
138432 1
4.2%
152748 1
4.2%
ValueCountFrequency (%)
15956729 1
4.2%
3593854 1
4.2%
2570268 1
4.2%
2410472 1
4.2%
1966372 1
4.2%
1415709 1
4.2%
537814 1
4.2%
415736 1
4.2%
358175 1
4.2%
337615 1
4.2%

CochesVendidos_2020
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1054119.3
Minimum18697
Maximum13602914
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size324.0 B
2023-11-27T18:18:51.538728image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum18697
5-th percentile23706.55
Q182941.5
median187524
Q3551207
95-th percentile2781721.1
Maximum13602914
Range13584217
Interquartile range (IQR)468265.5

Descriptive statistics

Standard deviation2777113.5
Coefficient of variation (CV)2.6345343
Kurtosis20.130356
Mean1054119.3
Median Absolute Deviation (MAD)134865
Skewness4.3621857
Sum25298864
Variance7.7123596 × 1012
MonotonicityNot monotonic
2023-11-27T18:18:51.909816image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
274619 1
 
4.2%
21667 1
 
4.2%
1714894 1
 
4.2%
262611 1
 
4.2%
966878 1
 
4.2%
52553 1
 
4.2%
80381 1
 
4.2%
138697 1
 
4.2%
163364 1
 
4.2%
412650 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
18697 1
4.2%
21667 1
4.2%
35264 1
4.2%
45323 1
4.2%
52553 1
4.2%
80381 1
4.2%
83795 1
4.2%
99389 1
4.2%
106617 1
4.2%
123810 1
4.2%
ValueCountFrequency (%)
13602914 1
4.2%
2926093 1
4.2%
1963614 1
4.2%
1714894 1
4.2%
1450789 1
4.2%
966878 1
4.2%
412650 1
4.2%
322283 1
4.2%
274619 1
4.2%
262611 1
4.2%

CochesVendidos_2021
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1071395.5
Minimum21860
Maximum14101851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size324.0 B
2023-11-27T18:18:52.071384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum21860
5-th percentile26673.05
Q195607
median184766.5
Q3564269.25
95-th percentile2531012.8
Maximum14101851
Range14079991
Interquartile range (IQR)468662.25

Descriptive statistics

Standard deviation2868339.5
Coefficient of variation (CV)2.6771995
Kurtosis20.631301
Mean1071395.5
Median Absolute Deviation (MAD)117971
Skewness4.4274714
Sum25713492
Variance8.2273717 × 1012
MonotonicityNot monotonic
2023-11-27T18:18:52.234117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
286070 1
 
4.2%
23612 1
 
4.2%
1776985 1
 
4.2%
274811 1
 
4.2%
967323 1
 
4.2%
52978 1
 
4.2%
81309 1
 
4.2%
134775 1
 
4.2%
165172 1
 
4.2%
429918 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
21860 1
4.2%
23612 1
4.2%
44019 1
4.2%
44954 1
4.2%
52978 1
4.2%
81309 1
4.2%
100373 1
4.2%
106380 1
4.2%
117667 1
4.2%
129050 1
4.2%
ValueCountFrequency (%)
14101851 1
4.2%
2627208 1
4.2%
1985907 1
4.2%
1776985 1
4.2%
1533662 1
4.2%
967323 1
4.2%
429918 1
4.2%
288920 1
4.2%
286070 1
4.2%
274811 1
4.2%

CochesVendidos_2022
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean990352.04
Minimum23359
Maximum12947827
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size324.0 B
2023-11-27T18:18:52.397911image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum23359
5-th percentile29777.1
Q183496.5
median166385
Q3521246.25
95-th percentile2492225.9
Maximum12947827
Range12924468
Interquartile range (IQR)437749.75

Descriptive statistics

Standard deviation2637969.5
Coefficient of variation (CV)2.6636685
Kurtosis20.423142
Mean990352.04
Median Absolute Deviation (MAD)116861
Skewness4.4010524
Sum23768449
Variance6.9588831 × 1012
MonotonicityNot monotonic
2023-11-27T18:18:52.567278image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
226794 1
 
4.2%
27717 1
 
4.2%
1673864 1
 
4.2%
255811 1
 
4.2%
883701 1
 
4.2%
45371 1
 
4.2%
83522 1
 
4.2%
139067 1
 
4.2%
169705 1
 
4.2%
400428 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
23359 1
4.2%
27717 1
4.2%
41451 1
4.2%
42369 1
4.2%
45371 1
4.2%
83420 1
4.2%
83522 1
4.2%
106857 1
4.2%
108567 1
4.2%
124178 1
4.2%
ValueCountFrequency (%)
12947827 1
4.2%
2618944 1
4.2%
1774157 1
4.2%
1673864 1
4.2%
1352822 1
4.2%
883701 1
4.2%
400428 1
4.2%
279093 1
4.2%
255811 1
4.2%
226794 1
4.2%

Gasolina_2017
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2922083
Minimum0.551
Maximum1.565
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size324.0 B
2023-11-27T18:18:52.721146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.551
5-th percentile1.0529
Q11.183
median1.296
Q31.447
95-th percentile1.54675
Maximum1.565
Range1.014
Interquartile range (IQR)0.264

Descriptive statistics

Standard deviation0.22201566
Coefficient of variation (CV)0.17181104
Kurtosis4.207425
Mean1.2922083
Median Absolute Deviation (MAD)0.142
Skewness-1.5401341
Sum31.013
Variance0.049290955
MonotonicityNot monotonic
2023-11-27T18:18:52.873535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1.534 2
 
8.3%
1.194 1
 
4.2%
1.18 1
 
4.2%
1.465 1
 
4.2%
1.441 1
 
4.2%
1.235 1
 
4.2%
1.289 1
 
4.2%
1.303 1
 
4.2%
1.117 1
 
4.2%
1.482 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
0.551 1
4.2%
1.043 1
4.2%
1.109 1
4.2%
1.117 1
4.2%
1.138 1
4.2%
1.18 1
4.2%
1.184 1
4.2%
1.194 1
4.2%
1.235 1
4.2%
1.237 1
4.2%
ValueCountFrequency (%)
1.565 1
4.2%
1.549 1
4.2%
1.534 2
8.3%
1.482 1
4.2%
1.465 1
4.2%
1.441 1
4.2%
1.435 1
4.2%
1.403 1
4.2%
1.399 1
4.2%
1.355 1
4.2%

Diesel_2017
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.210125
Minimum0.647
Maximum1.543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size324.0 B
2023-11-27T18:18:53.018391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.647
5-th percentile1.03535
Q11.14475
median1.2215
Q31.29525
95-th percentile1.43785
Maximum1.543
Range0.896
Interquartile range (IQR)0.1505

Descriptive statistics

Standard deviation0.17130251
Coefficient of variation (CV)0.1415577
Kurtosis4.4445587
Mean1.210125
Median Absolute Deviation (MAD)0.0765
Skewness-1.2177317
Sum29.043
Variance0.029344549
MonotonicityNot monotonic
2023-11-27T18:18:53.158371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1.146 1
 
4.2%
1.034 1
 
4.2%
1.543 1
 
4.2%
1.441 1
 
4.2%
1.138 1
 
4.2%
1.236 1
 
4.2%
1.16 1
 
4.2%
1.141 1
 
4.2%
1.281 1
 
4.2%
1.081 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0.647 1
4.2%
1.034 1
4.2%
1.043 1
4.2%
1.081 1
4.2%
1.138 1
4.2%
1.141 1
4.2%
1.146 1
4.2%
1.155 1
4.2%
1.16 1
4.2%
1.178 1
4.2%
ValueCountFrequency (%)
1.543 1
4.2%
1.441 1
4.2%
1.42 1
4.2%
1.328 1
4.2%
1.303 1
4.2%
1.299 1
4.2%
1.294 1
4.2%
1.281 1
4.2%
1.279 1
4.2%
1.272 1
4.2%

Gasolina_2018
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2787917
Minimum0.523
Maximum1.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size324.0 B
2023-11-27T18:18:53.298442image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.523
5-th percentile1.0142
Q11.19325
median1.259
Q31.43275
95-th percentile1.5151
Maximum1.52
Range0.997
Interquartile range (IQR)0.2395

Descriptive statistics

Standard deviation0.22483946
Coefficient of variation (CV)0.1758218
Kurtosis4.3458364
Mean1.2787917
Median Absolute Deviation (MAD)0.1675
Skewness-1.6528898
Sum30.691
Variance0.050552781
MonotonicityNot monotonic
2023-11-27T18:18:53.446093image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1.212 2
 
8.3%
1.432 2
 
8.3%
1.52 1
 
4.2%
1.41 1
 
4.2%
1.228 1
 
4.2%
1.257 1
 
4.2%
1.09 1
 
4.2%
1.435 1
 
4.2%
1.128 1
 
4.2%
1.516 1
 
4.2%
Other values (12) 12
50.0%
ValueCountFrequency (%)
0.523 1
4.2%
1.001 1
4.2%
1.089 1
4.2%
1.09 1
4.2%
1.128 1
4.2%
1.137 1
4.2%
1.212 2
8.3%
1.228 1
4.2%
1.23 1
4.2%
1.257 1
4.2%
ValueCountFrequency (%)
1.52 1
4.2%
1.516 1
4.2%
1.51 1
4.2%
1.503 1
4.2%
1.484 1
4.2%
1.435 1
4.2%
1.432 2
8.3%
1.425 1
4.2%
1.41 1
4.2%
1.399 1
4.2%

Diesel_2018
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.268125
Minimum0.703
Maximum1.492
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size324.0 B
2023-11-27T18:18:53.587561image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.703
5-th percentile1.08505
Q11.20525
median1.288
Q31.3455
95-th percentile1.486
Maximum1.492
Range0.789
Interquartile range (IQR)0.14025

Descriptive statistics

Standard deviation0.1671038
Coefficient of variation (CV)0.13177234
Kurtosis4.7498917
Mean1.268125
Median Absolute Deviation (MAD)0.079
Skewness-1.5480975
Sum30.435
Variance0.027923679
MonotonicityNot monotonic
2023-11-27T18:18:53.743327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1.31 2
 
8.3%
1.299 2
 
8.3%
1.219 1
 
4.2%
1.461 1
 
4.2%
1.489 1
 
4.2%
1.492 1
 
4.2%
1.164 1
 
4.2%
1.266 1
 
4.2%
1.22 1
 
4.2%
1.175 1
 
4.2%
Other values (12) 12
50.0%
ValueCountFrequency (%)
0.703 1
4.2%
1.084 1
4.2%
1.091 1
4.2%
1.164 1
4.2%
1.175 1
4.2%
1.191 1
4.2%
1.21 1
4.2%
1.219 1
4.2%
1.22 1
4.2%
1.257 1
4.2%
ValueCountFrequency (%)
1.492 1
4.2%
1.489 1
4.2%
1.469 1
4.2%
1.461 1
4.2%
1.425 1
4.2%
1.368 1
4.2%
1.338 1
4.2%
1.318 1
4.2%
1.31 2
8.3%
1.299 2
8.3%

Gasolina_2019
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.341125
Minimum0.607
Maximum1.667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size324.0 B
2023-11-27T18:18:53.904528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.607
5-th percentile1.11185
Q11.22425
median1.354
Q31.49775
95-th percentile1.6233
Maximum1.667
Range1.06
Interquartile range (IQR)0.2735

Descriptive statistics

Standard deviation0.22546499
Coefficient of variation (CV)0.16811631
Kurtosis3.6904486
Mean1.341125
Median Absolute Deviation (MAD)0.1385
Skewness-1.3459168
Sum32.187
Variance0.050834462
MonotonicityNot monotonic
2023-11-27T18:18:54.062098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1.228 1
 
4.2%
1.106 1
 
4.2%
1.367 1
 
4.2%
1.49 1
 
4.2%
1.303 1
 
4.2%
1.302 1
 
4.2%
1.324 1
 
4.2%
1.145 1
 
4.2%
1.481 1
 
4.2%
1.157 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0.607 1
4.2%
1.106 1
4.2%
1.145 1
4.2%
1.157 1
4.2%
1.167 1
4.2%
1.213 1
4.2%
1.228 1
4.2%
1.244 1
4.2%
1.302 1
4.2%
1.303 1
4.2%
ValueCountFrequency (%)
1.667 1
4.2%
1.629 1
4.2%
1.591 1
4.2%
1.578 1
4.2%
1.532 1
4.2%
1.521 1
4.2%
1.49 1
4.2%
1.481 1
4.2%
1.431 1
4.2%
1.392 1
4.2%

Diesel_2019
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2849167
Minimum0.725
Maximum1.543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size324.0 B
2023-11-27T18:18:54.209117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.725
5-th percentile1.111
Q11.2155
median1.2925
Q31.39
95-th percentile1.4713
Maximum1.543
Range0.818
Interquartile range (IQR)0.1745

Descriptive statistics

Standard deviation0.16652821
Coefficient of variation (CV)0.12960234
Kurtosis4.5093734
Mean1.2849167
Median Absolute Deviation (MAD)0.095
Skewness-1.5431664
Sum30.838
Variance0.027731645
MonotonicityNot monotonic
2023-11-27T18:18:54.366244image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1.111 2
 
8.3%
1.199 1
 
4.2%
1.474 1
 
4.2%
1.41 1
 
4.2%
1.543 1
 
4.2%
1.221 1
 
4.2%
1.246 1
 
4.2%
1.232 1
 
4.2%
1.18 1
 
4.2%
1.369 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
0.725 1
4.2%
1.111 2
8.3%
1.18 1
4.2%
1.189 1
4.2%
1.199 1
4.2%
1.221 1
4.2%
1.232 1
4.2%
1.234 1
4.2%
1.24 1
4.2%
1.246 1
4.2%
ValueCountFrequency (%)
1.543 1
4.2%
1.474 1
4.2%
1.456 1
4.2%
1.432 1
4.2%
1.41 1
4.2%
1.393 1
4.2%
1.389 1
4.2%
1.384 1
4.2%
1.383 1
4.2%
1.369 1
4.2%

Gasolina_2020
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2001667
Minimum0.485
Maximum1.561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size324.0 B
2023-11-27T18:18:54.538160image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.485
5-th percentile0.9087
Q11.0545
median1.2295
Q31.39225
95-th percentile1.4541
Maximum1.561
Range1.076
Interquartile range (IQR)0.33775

Descriptive statistics

Standard deviation0.23813965
Coefficient of variation (CV)0.19842215
Kurtosis2.0235147
Mean1.2001667
Median Absolute Deviation (MAD)0.17
Skewness-1.084322
Sum28.804
Variance0.056710493
MonotonicityNot monotonic
2023-11-27T18:18:54.696732image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1.072 1
 
4.2%
0.897 1
 
4.2%
1.36 1
 
4.2%
1.388 1
 
4.2%
1.185 1
 
4.2%
1.003 1
 
4.2%
1.194 1
 
4.2%
0.975 1
 
4.2%
1.405 1
 
4.2%
1.01 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0.485 1
4.2%
0.897 1
4.2%
0.975 1
4.2%
1.003 1
4.2%
1.01 1
4.2%
1.023 1
4.2%
1.065 1
4.2%
1.071 1
4.2%
1.072 1
4.2%
1.185 1
4.2%
ValueCountFrequency (%)
1.561 1
4.2%
1.458 1
4.2%
1.432 1
4.2%
1.428 1
4.2%
1.419 1
4.2%
1.405 1
4.2%
1.388 1
4.2%
1.36 1
4.2%
1.358 1
4.2%
1.281 1
4.2%

Diesel_2020
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1200417
Minimum0.57
Maximum1.405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size324.0 B
2023-11-27T18:18:54.856535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.57
5-th percentile0.9036
Q11.04825
median1.0925
Q31.2475
95-th percentile1.389
Maximum1.405
Range0.835
Interquartile range (IQR)0.19925

Descriptive statistics

Standard deviation0.179528
Coefficient of variation (CV)0.16028689
Kurtosis2.646355
Mean1.1200417
Median Absolute Deviation (MAD)0.1045
Skewness-0.98657873
Sum26.881
Variance0.032230303
MonotonicityNot monotonic
2023-11-27T18:18:55.002919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1.043 1
 
4.2%
0.891 1
 
4.2%
1.404 1
 
4.2%
1.405 1
 
4.2%
1.069 1
 
4.2%
1.067 1
 
4.2%
1.062 1
 
4.2%
0.977 1
 
4.2%
1.262 1
 
4.2%
1.004 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0.57 1
4.2%
0.891 1
4.2%
0.975 1
4.2%
0.977 1
4.2%
1.004 1
4.2%
1.043 1
4.2%
1.05 1
4.2%
1.051 1
4.2%
1.062 1
4.2%
1.067 1
4.2%
ValueCountFrequency (%)
1.405 1
4.2%
1.404 1
4.2%
1.304 1
4.2%
1.296 1
4.2%
1.262 1
4.2%
1.261 1
4.2%
1.243 1
4.2%
1.235 1
4.2%
1.186 1
4.2%
1.181 1
4.2%

Gasolina_2021
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4943333
Minimum0.765
Maximum1.963
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size324.0 B
2023-11-27T18:18:55.149511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.765
5-th percentile1.19675
Q11.333
median1.481
Q31.678
95-th percentile1.7895
Maximum1.963
Range1.198
Interquartile range (IQR)0.345

Descriptive statistics

Standard deviation0.25769103
Coefficient of variation (CV)0.17244548
Kurtosis1.4001473
Mean1.4943333
Median Absolute Deviation (MAD)0.1885
Skewness-0.75488692
Sum35.864
Variance0.066404667
MonotonicityNot monotonic
2023-11-27T18:18:55.309109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1.398 1
 
4.2%
1.193 1
 
4.2%
1.346 1
 
4.2%
1.671 1
 
4.2%
1.476 1
 
4.2%
1.286 1
 
4.2%
1.465 1
 
4.2%
1.218 1
 
4.2%
1.664 1
 
4.2%
1.242 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0.765 1
4.2%
1.193 1
4.2%
1.218 1
4.2%
1.242 1
4.2%
1.286 1
4.2%
1.294 1
4.2%
1.346 1
4.2%
1.398 1
4.2%
1.435 1
4.2%
1.44 1
4.2%
ValueCountFrequency (%)
1.963 1
4.2%
1.794 1
4.2%
1.764 1
4.2%
1.737 1
4.2%
1.722 1
4.2%
1.699 1
4.2%
1.671 1
4.2%
1.664 1
4.2%
1.659 1
4.2%
1.635 1
4.2%

Diesel_2021
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.422875
Minimum0.844
Maximum1.829
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size324.0 B
2023-11-27T18:18:55.459682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.844
5-th percentile1.21515
Q11.33675
median1.4295
Q31.533
95-th percentile1.64305
Maximum1.829
Range0.985
Interquartile range (IQR)0.19625

Descriptive statistics

Standard deviation0.19205396
Coefficient of variation (CV)0.13497599
Kurtosis2.804012
Mean1.422875
Median Absolute Deviation (MAD)0.0995
Skewness-0.8239608
Sum34.149
Variance0.036884723
MonotonicityNot monotonic
2023-11-27T18:18:55.614281image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1.389 1
 
4.2%
1.216 1
 
4.2%
1.46 1
 
4.2%
1.829 1
 
4.2%
1.344 1
 
4.2%
1.398 1
 
4.2%
1.369 1
 
4.2%
1.215 1
 
4.2%
1.503 1
 
4.2%
1.25 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0.844 1
4.2%
1.215 1
4.2%
1.216 1
4.2%
1.25 1
4.2%
1.288 1
4.2%
1.333 1
4.2%
1.338 1
4.2%
1.344 1
4.2%
1.369 1
4.2%
1.389 1
4.2%
ValueCountFrequency (%)
1.829 1
4.2%
1.651 1
4.2%
1.598 1
4.2%
1.597 1
4.2%
1.587 1
4.2%
1.536 1
4.2%
1.532 1
4.2%
1.518 1
4.2%
1.503 1
4.2%
1.478 1
4.2%

Gasolina_2022
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5339583
Minimum0.769
Maximum1.851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size324.0 B
2023-11-27T18:18:55.762570image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.769
5-th percentile1.28835
Q11.44275
median1.566
Q31.699
95-th percentile1.82655
Maximum1.851
Range1.082
Interquartile range (IQR)0.25625

Descriptive statistics

Standard deviation0.23536253
Coefficient of variation (CV)0.15343476
Kurtosis3.641125
Mean1.5339583
Median Absolute Deviation (MAD)0.137
Skewness-1.3917357
Sum36.815
Variance0.05539552
MonotonicityNot monotonic
2023-11-27T18:18:55.918282image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1.454 1
 
4.2%
1.287 1
 
4.2%
1.49 1
 
4.2%
1.695 1
 
4.2%
1.565 1
 
4.2%
1.318 1
 
4.2%
1.487 1
 
4.2%
1.296 1
 
4.2%
1.6 1
 
4.2%
1.409 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0.769 1
4.2%
1.287 1
4.2%
1.296 1
4.2%
1.318 1
4.2%
1.331 1
4.2%
1.409 1
4.2%
1.454 1
4.2%
1.485 1
4.2%
1.487 1
4.2%
1.49 1
4.2%
ValueCountFrequency (%)
1.851 1
4.2%
1.827 1
4.2%
1.824 1
4.2%
1.757 1
4.2%
1.747 1
4.2%
1.711 1
4.2%
1.695 1
4.2%
1.655 1
4.2%
1.625 1
4.2%
1.6 1
4.2%

Diesel_2022
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6711667
Minimum1.128
Maximum2.118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size324.0 B
2023-11-27T18:18:56.065989image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.128
5-th percentile1.5262
Q11.58625
median1.6535
Q31.76
95-th percentile1.95995
Maximum2.118
Range0.99
Interquartile range (IQR)0.17375

Descriptive statistics

Standard deviation0.18392595
Coefficient of variation (CV)0.11005841
Kurtosis3.4426432
Mean1.6711667
Median Absolute Deviation (MAD)0.098
Skewness-0.32221785
Sum40.108
Variance0.033828754
MonotonicityNot monotonic
2023-11-27T18:18:56.211163image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1.654 1
 
4.2%
1.525 1
 
4.2%
1.553 1
 
4.2%
2.118 1
 
4.2%
1.643 1
 
4.2%
1.545 1
 
4.2%
1.616 1
 
4.2%
1.533 1
 
4.2%
1.607 1
 
4.2%
1.653 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1.128 1
4.2%
1.525 1
4.2%
1.533 1
4.2%
1.545 1
4.2%
1.553 1
4.2%
1.56 1
4.2%
1.595 1
4.2%
1.597 1
4.2%
1.607 1
4.2%
1.616 1
4.2%
ValueCountFrequency (%)
2.118 1
4.2%
1.985 1
4.2%
1.818 1
4.2%
1.811 1
4.2%
1.791 1
4.2%
1.778 1
4.2%
1.754 1
4.2%
1.749 1
4.2%
1.712 1
4.2%
1.693 1
4.2%

Interactions

2023-11-27T18:18:47.192547image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:09.416584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:12.116675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2023-11-27T18:18:13.648990image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:16.012378image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:18.220655image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:20.526626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:22.726624image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:24.919955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:27.084253image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:29.066182image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:31.238809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:33.421063image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:35.744754image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:37.822605image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:39.965164image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:42.078925image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:44.378880image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:46.436124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:48.527763image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:11.397766image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:13.808492image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:16.145070image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:18.346206image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:20.653184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:22.880275image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:25.045252image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:27.195234image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:29.193359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:31.367480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:33.556238image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:35.866413image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:37.955279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:40.081853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:42.209127image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:44.501525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:46.555806image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:48.633465image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:11.518478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:14.003487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:16.261757image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:18.463891image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:20.766412image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:23.040443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:25.153429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:27.297468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:29.298624image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:31.481682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:33.670944image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:35.977656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:38.072966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:40.188662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:42.327913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:44.608307image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:46.660525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:48.732172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:11.631215image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:14.132188image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:16.373955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:18.572601image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:20.879655image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:23.169852image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:25.258293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:27.399786image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:29.396944image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:31.592889image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:33.802580image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:36.091354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:38.186700image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:40.284860image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:42.443603image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:44.741950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:46.767781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:48.842797image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:11.762197image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:14.272185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:16.496894image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:18.694338image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:20.997060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:23.298740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:25.368997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:27.506482image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:29.507530image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:31.709819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:33.924195image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:36.208687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:38.312460image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:40.390086image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:42.566235image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:44.857641image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:46.879490image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:48.943487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:11.883334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:14.401104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:16.616575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:18.986614image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:21.110758image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:23.420456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:25.477172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:27.610689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:29.665107image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:31.824513image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:34.039886image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:36.322343image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:38.434169image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:40.494317image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:42.688906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:44.967854image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:46.989283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:49.045925image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:12.003473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:14.525743image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:16.731608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:19.096009image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:21.221040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:23.540095image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:25.590820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:27.709424image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:29.782328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:31.939639image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:34.150246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:36.435040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:38.557089image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:40.600540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:42.810582image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:45.077015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:18:47.093811image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-27T18:18:56.335828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
CochesVendidos_2017CochesVendidos_2018CochesVendidos_2019CochesVendidos_2020CochesVendidos_2021CochesVendidos_2022Diesel_2017Diesel_2018Diesel_2019Diesel_2020Diesel_2021Diesel_2022Gasolina_2017Gasolina_2018Gasolina_2019Gasolina_2020Gasolina_2021Gasolina_2022
CochesVendidos_20171.0000.9940.9920.9920.9930.9880.1080.1680.1610.2740.2050.1230.1870.1950.1220.1850.0560.120
CochesVendidos_20180.9941.0000.9980.9960.9970.9940.0860.1460.1430.2670.1830.1050.1680.1750.1010.1630.0300.096
CochesVendidos_20190.9920.9981.0000.9980.9970.9910.0790.1400.1410.2620.1700.1190.1610.1670.0970.1590.0230.101
CochesVendidos_20200.9920.9960.9981.0000.9970.9900.0800.1380.1390.2580.1700.1330.1690.1710.1040.1700.0320.113
CochesVendidos_20210.9930.9970.9970.9971.0000.9970.0780.1370.1320.2470.1700.1140.1690.1730.1030.1640.0300.103
CochesVendidos_20220.9880.9940.9910.9900.9971.0000.0900.1480.1400.2570.1760.1030.1870.1870.1140.1750.0370.103
Diesel_20170.1080.0860.0790.0800.0780.0901.0000.9720.9350.9100.8170.5580.8750.8530.8370.8270.7220.697
Diesel_20180.1680.1460.1400.1380.1370.1480.9721.0000.9630.8920.8300.6010.8420.8570.8400.8230.7200.723
Diesel_20190.1610.1430.1410.1390.1320.1400.9350.9631.0000.8970.8150.6270.8390.8590.8710.8330.7520.759
Diesel_20200.2740.2670.2620.2580.2470.2570.9100.8920.8971.0000.8620.5540.8620.8260.8120.8300.7220.659
Diesel_20210.2050.1830.1700.1700.1700.1760.8170.8300.8150.8621.0000.6500.8470.8430.8640.8360.8660.721
Diesel_20220.1230.1050.1190.1330.1140.1030.5580.6010.6270.5540.6501.0000.5610.5910.6990.6940.7620.881
Gasolina_20170.1870.1680.1610.1690.1690.1870.8750.8420.8390.8620.8470.5611.0000.9680.9500.9510.8590.759
Gasolina_20180.1950.1750.1670.1710.1730.1870.8530.8570.8590.8260.8430.5910.9681.0000.9660.9640.8920.815
Gasolina_20190.1220.1010.0970.1040.1030.1140.8370.8400.8710.8120.8640.6990.9500.9661.0000.9680.9490.875
Gasolina_20200.1850.1630.1590.1700.1640.1750.8270.8230.8330.8300.8360.6940.9510.9640.9681.0000.9300.867
Gasolina_20210.0560.0300.0230.0320.0300.0370.7220.7200.7520.7220.8660.7620.8590.8920.9490.9301.0000.894
Gasolina_20220.1200.0960.1010.1130.1030.1030.6970.7230.7590.6590.7210.8810.7590.8150.8750.8670.8941.000

Missing values

2023-11-27T18:18:49.218864image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-27T18:18:49.554384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CountryCochesVendidos_2017CochesVendidos_2018CochesVendidos_2019CochesVendidos_2020CochesVendidos_2021CochesVendidos_2022Gasolina_2017Diesel_2017Gasolina_2018Diesel_2018Gasolina_2019Diesel_2019Gasolina_2020Diesel_2020Gasolina_2021Diesel_2021Gasolina_2022Diesel_2022
0Austria3791843703153581752746192860702267941.1941.1461.2121.2191.2281.1991.0721.0431.3981.3891.4541.654
1Bulgaria2422330218317262166723612277171.0431.0341.0011.0911.1061.1110.8970.8911.1931.2161.2871.525
2Croatia4426458557617073526444019414511.2711.2071.2581.2771.3411.3311.2151.1861.4861.4781.3311.595
3Czech Republic2825402721342625642116842143271963601.1841.1551.2301.2571.2441.2401.0651.0511.4351.3991.4961.560
4Denmark2517632446892492492202782043611630651.5341.2941.5031.3381.6291.3891.4581.2351.7641.5361.8241.811
5Estonia2483425581268391869721860233591.2371.2371.2601.3101.3921.3931.2441.0501.5121.3331.7111.712
6Finland12246412561811769199389100373834201.4351.3281.4841.4691.5321.4321.4191.2961.7941.6511.8511.985
7France2439778251908025702681963614198590717741571.4031.2791.4321.4251.5211.4561.3581.2611.6351.5321.6551.749
8Germany3419716342836735938542926093262720826189441.3551.1801.4251.2991.3711.2541.2811.1121.6591.5181.7471.818
9Greece90447105652116651837951063801085671.5341.3031.5101.3681.5911.3831.4321.1601.7371.4771.8271.791
CountryCochesVendidos_2017CochesVendidos_2018CochesVendidos_2019CochesVendidos_2020CochesVendidos_2021CochesVendidos_2022Gasolina_2017Diesel_2017Gasolina_2018Diesel_2018Gasolina_2019Diesel_2019Gasolina_2020Diesel_2020Gasolina_2021Diesel_2021Gasolina_2022Diesel_2022
14Netherlands3864424100034157363222832889202790931.5651.2721.5161.2991.6671.3841.5611.2431.9631.5971.7571.754
15Poland4729215148895378144126504299184004281.1091.0811.1281.1911.1571.1891.0101.0041.2421.2501.4091.653
16Portugal2433602512352455111633641651721697051.4821.2811.4351.3101.4811.3691.4051.2621.6641.5031.6001.607
17Romania1462781759921903991386971347751390671.1171.1411.0901.1751.1451.1800.9750.9771.2181.2151.2961.533
18Slovakia1009891044071068758038181309835221.3031.1601.2571.2201.3241.2321.1941.0621.4651.3691.4871.616
19Slovenia7030472013718505255352978453711.2891.2361.2281.2661.3021.2461.0031.0671.2861.3981.3181.545
20Spain1372519147868114157099668789673238837011.2351.1381.2121.1641.3031.2211.1851.0691.4761.3441.5651.643
21Sweden3469513296293376152626112748112558111.4411.4411.4101.4921.4901.5431.3881.4051.6711.8291.6952.118
22UK2630610245802224104721714894177698516738641.4651.5431.4321.4891.3671.4101.3601.4041.3461.4601.4901.553
23USA1618868016263975159567291360291414101851129478270.5510.6470.5230.7030.6070.7250.4850.5700.7650.8440.7691.128